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Blockchain-based decentralized model for mobile crowd-sensing

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Abstract

In recent years, with the development of science and technology, mobile intelligent devices have become more and more common. At the same time, the sensors carried on a mobile intelligent device are becoming more and more various, which makes the Mobile Crowd-Sensing (MCS) possible to develop. MCS abandons the traditional one-to-one outsourcing but turns outsourcing users into all groups that use mobile intelligent devices with a more advantageous number of people and wider geographical distribution. Meanwhile, it also reduces the cost. However, most of the current common MCS adopt a centralized structure. This makes the edge node heavily dependent on the central node and makes the process faced with the problems such as high cost and susceptibility to malicious attacks. In addition, in fact, there is no fully trusted central service provider. Once the center does some measures to endanger others, it will cause an unimaginable result. In this regard, we propose a decentralized trust model based on blockchain. In this model, if a transaction needs to be processed, the information of the transaction will not be stored in only one node(like central node), but in all nodes. At this time, a specific third party is no longer required to supervise the transaction. In other words, each node in the blockchain is the transaction supervisor. After that, we implement a decentralized MCS platform. Finally, we do some experiments to verify the availability and stability of the decentralized model.

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Data availability

In our work, we proposed a decentralized MCS model and verified its feasibility through simulation experiments. Therefore, the results presented in the experiment are all from our simulation experiments. If readers need specific data on the experimental results, they can contact corresponding author through email.

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Correspondence to Yilin Yang.

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Hao, D., Wang, E., Liu, W. et al. Blockchain-based decentralized model for mobile crowd-sensing. CCF Trans. Pervasive Comp. Interact. 6, 68–81 (2024). https://doi.org/10.1007/s42486-023-00140-x

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